1. Technical Field
The present invention relates to the field of content recommendations, i.e. the methods that attempt to present contents, such as films, music files, videos, books, news, images, web pages, that are likely of interest to the user.
2. Description of the Related Art
Many recommendation techniques are known: some of them provide recommendations based on the user's predilections expressed by means of explicit votes, while other techniques are based on the observation of the contents that a user chooses. The online DVD rental service Netflix (www.netflix.com) is of the first type and operates by recommending users a list of films which have not yet been rented. The list of films is estimated by comparing previous user's votes with the ones of other users.
The Internet Movie Database (IMDb) is an online database of information related to films, actors, television shows, production crew personnel, video games, and most recently, fictional characters featured in visual entertainment media. This database employs another recommendation technique which is based on the contents and does not exploit the user's predilections.
Recommendation techniques based on the content generally employ texts describing in written form the contents and use information retrieval methods to determine relations between contents. Document “Hybrid pre-query term expansion using Latent Semantic Analysis”, Laurence A. F. Park, Kotagiri Ramamohanarao, Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM'04) describes an information retrieval method in which a query map using single Value Decomposition and Latent Semantic Analysis is built.
Document “Recommending from Content: Preliminary Results from an E-Commerce Experiment”, Mark Rosenstein, Carol Lochbaum, Conference on Human Factors in Computing Systems (CHI '00) discloses the effects of various forms of recommendations on consumer behaviour at a web site using also Latent Semantic Indexing. H. Zha and H. Simon in “On updating problems in latent semantic indexing”, SIAM Journal of Scientific Computing, vol. 21, pp. 782-791, 1999″ and M. Brand in “Fast low-rank modifications of the thin singular value decomposition”, Linear Algebra and its Applications, vol. 415, pp. 20-30, 2006″ have disclosed an incremental LSA technique according to which additive modifications of a singular value decomposition (SVD) to reflect updates, downdates and edits of the data matrix is developed.
Rocha Luis M., Johan Bollen in “Biologically Motivated Distributed Designs for Adaptive Knowledge Management, Design Principles for the Immune System and other Distributed Autonomous Systems”, L. Segel and I. Cohen (Eds.) Santa Fe Institute Series in the Sciences of Complexity; Oxford University Press, pp. 305-334. 2001 discuss the adaptive recommendation systems TalkMine and @ApWeb that allow users to obtain an active, evolving interaction with information resources.
Badrul M. Sarwar, George Karypis, Joseph A. Konstan, John T. Riedl in “Application of Dimensionality Reduction in Recommender System”—A Case Study”, ACM WebKDD Workshop, 2000, disclose a technique based on the Singular Value Decomposition permitting to improve the scalability of recommender systems. Document US2006/0259481 refers to a method of analysing documents in the field of the information retrieval which uses a Latent Semantic Analysis to infer semantic relations between terms and to achieve dimensionality reduction of processed matrices.